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Documentation: Life Insurance - LDEx Data Quality Analysis Package: How the Artifacts Connect

  • Dataprepr Team
  • Nov 22
  • 6 min read

The Complete Picture: Artifact Relationships


Think of this toolkit as a complete data quality system where each file plays a specific role. Everything connects to help you answer one question: "Is my LDEx enrollment data good enough to load into production?" The toolkit gives you an objective, comprehensive, repeatable answer to that question - every single time.


Here's how they all connect:



                    ┌─────────────────────────────┐
                    │   YOUR LDEx XML FILE        │
                    │  (enrollment data)          │
                    └──────────┬──────────────────┘
                               │
                               ▼
                    ┌─────────────────────────────┐
                    │  ldex_data_quality_         │
                    │  analysis.R                 │◄─────┐
                    │  (THE ANALYZER)             │      │
                    └──────────┬──────────────────┘      │
                               │                         │
                               ▼                         │
                    ┌─────────────────────────────┐      │
                    │  Data Quality Report.xlsx   │      │
                    │  (THE OUTPUT)               │      │
                    └─────────────────────────────┘      │
                                                         │
         Documentation & Support Files ─────────────────┘
         • README.md ...................... Quick start guide
         • DATA_QUALITY_ANALYSIS_GUIDE.md . Full methodology
         • PACKAGE_INDEX.md ............... File reference
         • ADVANCED_LLM_PROMPT.md ......... AI analysis helper
         • sample_ldex_life_enrollment.xml. Example data
         • LDEx_Data_Quality_Report_Sample.xlsx ... Example output
         • generate_sample_report.py ...... Demo generator

The Three Core Components


1. THE INPUT → sample_ldex_life_enrollment.xml

  • What it is: Example LDEx enrollment file

  • Purpose: Shows you what data format the analyzer expects

  • Links to: The R script reads this (or your real file)

  • How to use: Replace this with your actual LDEx files


2. THE ANALYZER → ldex_data_quality_analysis.R

  • What it is: 700+ lines of R code that does all the work

  • Purpose: Reads XML, runs 30+ checks, generates Excel report

  • Links to:

    • Reads: Your LDEx XML file

    • Outputs: Excel report

    • References: All documentation files for methodology

  • How to use: Configure file paths, then run it


3. THE OUTPUT → LDEx_Data_Quality_Report.xlsx

  • What it is: 10-sheet comprehensive analysis report

  • Purpose: Shows all findings in business-friendly format

  • Links to: Generated by the R script

  • How to use: Open in Excel, review findings, take action


The Supporting Cast


Documentation Trio

README.md (The Quick Start)

  • Role: Your first stop

  • Links to: Points you to all other files

  • Use when: You're getting started or need quick reference

DATA_QUALITY_ANALYSIS_GUIDE.md (The Deep Dive)

  • Role: Complete methodology explanation

  • Links to: References the R script code blocks

  • Use when: You need to understand HOW it works

PACKAGE_INDEX.md (The Reference)

  • Role: Complete file catalog and navigation

  • Links to: Everything - it's the master index

  • Use when: You need to find something specific


Demo Files

LDEx_Data_Quality_Report_Sample.xlsx (The Preview)

  • Role: Shows you what the output looks like

  • Links to: This is what the R script produces

  • Use when: You want to see results WITHOUT running code

generate_sample_report.py (The Generator)

  • Role: Creates the sample Excel report

  • Links to: Generates the sample report file

  • Use when: You want to recreate the demo report


AI Helper

ADVANCED_LLM_PROMPT.md (The AI Guide)

  • Role: Instructions for using AI to analyze the report

  • Links to: Designed to work with the Excel output

  • Use when: You want AI assistance interpreting results


Three Ways to Use This Toolkit

OPTION 1: "Just Show Me" (5 minutes)

For business users who want to see what's possible

1. Open: LDEx_Data_Quality_Report_Sample.xlsx
2. Look at: Sheet 1 (Executive Summary)
3. Notice: Overall score, grade, key findings
4. Done! You've seen what the output looks like

Files used:

  • ✓ LDEx_Data_Quality_Report_Sample.xlsx

  • ✓ README.md (for interpretation)


OPTION 2: "Run It on My Data" (30 minutes)

For analysts who want to analyze their own LDEx files

Step 1: Install R and packages
   → Follow instructions in README.md
   
Step 2: Prepare your LDEx file
   → Place your XML file in a known location
   → Example: /data/my_enrollment.xml
   
Step 3: Configure the R script
   → Open: ldex_data_quality_analysis.R
   → Line 36: Set ldex_file path to YOUR file
   → Line 39: Set output_excel name
   
Step 4: Run the analysis
   → In R: source("ldex_data_quality_analysis.R")
   → Wait 5 seconds to 5 minutes (depending on file size)
   
Step 5: Review your report
   → Open: LDEx_Data_Quality_Report.xlsx
   → Start with Executive Summary sheet
   → Investigate any RED (fail) items

Files used:

  • ✓ ldex_data_quality_analysis.R (THE MAIN SCRIPT)

  • ✓ Your LDEx XML file

  • ✓ README.md (for setup instructions)

  • ✓ DATA_QUALITY_ANALYSIS_GUIDE.md (for understanding results)


Files produced:

  • ✓ LDEx_Data_Quality_Report.xlsx (YOUR RESULTS)


OPTION 3: "Customize It" (2+ hours)

For developers who want to extend the toolkit

Step 1: Understand the baseline
   → Read: DATA_QUALITY_ANALYSIS_GUIDE.md (full methodology)
   → Study: ldex_data_quality_analysis.R (code structure)
   → Review: sample_ldex_life_enrollment.xml (data format)
   
Step 2: Run baseline analysis
   → Follow Option 2 steps above
   → Understand what the default analysis does
   
Step 3: Add your custom rules
   → Reference: DATA_QUALITY_ANALYSIS_GUIDE.md (Customization Guide section)
   → Examples included for:
     - New validation rules
     - Custom anomalies
     - Additional business rules
   
Step 4: Test your changes
   → Run on sample data first
   → Verify new checks appear in output
   
Step 5: Deploy to production
   → Document your changes
   → Add to your data pipeline

Files used:

  • ✓ ALL of them! You're going deep


The Information Flow

How Data Moves Through the System

1. YOU provide ────────► LDEx XML file
                         (Your enrollment data)
                                │
                                ▼
2. R Script reads ─────► Parses XML using xml2 library
                         Extracts: Members, Coverages, Dependents
                                │
                                ▼
3. Analysis runs ──────► Completeness Check
                         Validation Rules (10+)
                         Anomaly Detection (8+)
                         Business Rules (5+)
                         Statistical Profiling
                                │
                                ▼
4. Scoring happens ────► Weighted score calculation
                         Component scores ───► Score (0-100%)
                         Grade assignment ───► Letter grade (A-D)
                                │
                                ▼
5. Excel export ───────► 10 sheets created
                         Color coding applied
                         Formulas calculated
                                │
                                ▼
6. YOU receive ────────► LDEx_Data_Quality_Report.xlsx
                         (Ready to review and act on)

File Dependency Map

What Needs What


To RUN the analysis, you need:

  • ldex_data_quality_analysis.R ← THE ESSENTIAL FILE

  • Your LDEx XML file

  • R installed with required packages


To UNDERSTAND the analysis, you need:

  • README.md (quick start)

  • DATA_QUALITY_ANALYSIS_GUIDE.md (full details)


To SEE example output, you need:

  • LDEx_Data_Quality_Report_Sample.xlsx


To NAVIGATE everything, you need:

  • PACKAGE_INDEX.md


To USE AI for analysis, you need:

  • ADVANCED_LLM_PROMPT.md

  • Your actual Excel report


Common Usage Workflows

Workflow 1: First-Time User

1. README.md ..................... Understand what this is
2. LDEx_Data_Quality_Report_Sample.xlsx ... See example output
3. DATA_QUALITY_ANALYSIS_GUIDE.md . Learn the methodology
4. Install R and packages ........ Get environment ready
5. Run on sample_ldex_life_enrollment.xml ... Test it works
6. Run on YOUR data .............. Analyze real files

Workflow 2: Regular User

1. Place new LDEx file in folder
2. Run: source("ldex_data_quality_analysis.R")
3. Open: LDEx_Data_Quality_Report.xlsx
4. Check: Sheet 4 (Validation Results) for any FAILS
5. Check: Sheet 5 (Anomaly Detection) for HIGH severity
6. Make decisions based on findings

Workflow 3: Developer Customizing

1. Review: Current validation rules in R script
2. Identify: What additional checks you need
3. Reference: Customization examples in GUIDE
4. Code: Add new rules to R script
5. Test: Run on sample data
6. Document: Update README with your additions
7. Deploy: To production pipeline

Workflow 4: Executive Review

1. Receive: Excel report from analyst
2. Open: Sheet 1 (Executive Summary)
3. Look at: Overall Score and Grade
4. Decide: 
   - Grade A/B → Approve for loading
   - Grade C → Review with team
   - Grade D → Reject, send back to source

Key Connections to Remember

1. R Script ↔ XML File

The R script's first 100 lines are dedicated to:
- Finding your XML file (line 36)
- Parsing it correctly (lines 95-99)
- Extracting data safely (lines 46-54)

CONNECTION: You MUST update line 36 with your file path

2. R Script ↔ Excel Output

The R script's last 200 lines create:
- All 10 Excel sheets
- Color formatting
- Data validation rules
- Summary calculations

CONNECTION: Line 39 determines output file name

3. Documentation ↔ Code

Every validation rule in the R script is explained in:
- DATA_QUALITY_ANALYSIS_GUIDE.md (lines 52-86)
- With examples of what passes/fails
- With business rationale

CONNECTION: Section numbers match rule IDs

4. Sample Files ↔ Real Usage

sample_ldex_life_enrollment.xml shows the structure
Your real file should match this structure
Otherwise, namespace errors occur

CONNECTION: Same XML structure = analysis works

The "Don't Forget" Checklist

When using this toolkit, remember these connections:

☐ The R script needs the XML file path (line 36)

☐ The R script creates the Excel output (line 39)

☐ The documentation explains the code (reference guide)

☐ The sample output shows expected results (preview)

☐ The README is your quick reference (start here)

☐ The GUIDE has customization examples (for extending)

☐ The sample XML is your format reference (structure guide)


Quick Reference: "Which File Do I Need?"

I want to...Use this file
Get started quickly         README.md
See example output          LDEx_Data_Quality_Report_Sample.xlsx
Run analysis                ldex_data_quality_analysis.R
Understand methodology      DATA_QUALITY_ANALYSIS_GUIDE.mdFind a specific feature            PACKAGE_INDEX.md
Customize validation rules  DATA_QUALITY_ANALYSIS_GUIDE.md + R scriptUse AI for analysis   ADVANCED_LLM_PROMPT.md
Understand XML format       sample_ldex_life_enrollment.xml
Regenerate sample report    generate_sample_report.py

The Bottom Line


Think of it this way:

  • Documentation files = Your instruction manual

  • R script = The engine that does the work

  • Sample files = Training wheels to learn

  • Your XML file = The fuel you put in

  • Excel output = The insights you get out



For more help, reach out to info@dataprepr.ai


 
 
 

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